利用区域增长分割、GLCM和离散小波变换提取医学影像特征的大数据演变

Y. Gupta
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引用次数: 0

摘要

大数据是指来自各种来源(社交媒体、医疗保健、不同传感器等)的海量数据,速度非常快。随着娱乐网络、监视摄像机、卫星图像、医学图像的扩大,多媒体或图像数据迅速增长。医疗保健是大数据最有希望改变人类生活的领域。分析复杂数据的过程通常涉及隐藏模式的揭示。在医疗保健领域捕捉任何医学图像的视觉背景,提取是一个很好的介绍在数字图像处理词。本研究的目的是详细概述医疗保健中的大数据以及利用区域增长分割、GLCM和离散小波变换等特征提取技术处理非侵入性医学图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform
Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.
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